Overview

Dataset statistics

Number of variables27
Number of observations9338
Missing cells136275
Missing cells (%)54.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 MiB
Average record size in memory216.0 B

Variable types

Numeric16
Categorical10
Unsupported1

Alerts

bmirecum has constant value ""Constant
bmileg has constant value ""Constant
bmiarml has constant value ""Constant
bmiarmc has constant value ""Constant
bmiwaist has constant value ""Constant
Unnamed: 0 is highly overall correlated with seqnHigh correlation
seqn is highly overall correlated with Unnamed: 0High correlation
bmxwt is highly overall correlated with bmxrecum and 12 other fieldsHigh correlation
bmxrecum is highly overall correlated with bmxwt and 5 other fieldsHigh correlation
bmxhead is highly overall correlated with bmxwt and 3 other fieldsHigh correlation
bmxht is highly overall correlated with bmxwt and 7 other fieldsHigh correlation
bmxbmi is highly overall correlated with bmxwt and 10 other fieldsHigh correlation
bmxleg is highly overall correlated with bmxht and 1 other fieldsHigh correlation
bmxarml is highly overall correlated with bmxwt and 13 other fieldsHigh correlation
bmxarmc is highly overall correlated with bmxwt and 11 other fieldsHigh correlation
bmxwaist is highly overall correlated with bmxwt and 10 other fieldsHigh correlation
bmxsad1 is highly overall correlated with bmxwt and 8 other fieldsHigh correlation
bmxsad2 is highly overall correlated with bmxwt and 8 other fieldsHigh correlation
bmxsad3 is highly overall correlated with bmxwt and 9 other fieldsHigh correlation
bmxsad4 is highly overall correlated with bmxwt and 10 other fieldsHigh correlation
bmdavsad is highly overall correlated with bmxwt and 8 other fieldsHigh correlation
bmdstats is highly overall correlated with bmiwt and 2 other fieldsHigh correlation
bmiwt is highly overall correlated with bmxhead and 3 other fieldsHigh correlation
bmiht is highly overall correlated with bmxwt and 10 other fieldsHigh correlation
bmdsadcm is highly overall correlated with bmxsad4 and 2 other fieldsHigh correlation
bmdstats is highly imbalanced (72.7%)Imbalance
bmiwt is highly imbalanced (69.1%)Imbalance
bmdsadcm is highly imbalanced (67.7%)Imbalance
bmxwt has 95 (1.0%) missing valuesMissing
bmiwt has 8959 (95.9%) missing valuesMissing
bmxrecum has 8259 (88.4%) missing valuesMissing
bmirecum has 9307 (99.7%) missing valuesMissing
bmxhead has 9102 (97.5%) missing valuesMissing
bmihead has 9338 (100.0%) missing valuesMissing
bmxht has 723 (7.7%) missing valuesMissing
bmiht has 9070 (97.1%) missing valuesMissing
bmxbmi has 736 (7.9%) missing valuesMissing
bmdbmic has 5983 (64.1%) missing valuesMissing
bmxleg has 2383 (25.5%) missing valuesMissing
bmileg has 8984 (96.2%) missing valuesMissing
bmxarml has 512 (5.5%) missing valuesMissing
bmiarml has 8969 (96.0%) missing valuesMissing
bmxarmc has 512 (5.5%) missing valuesMissing
bmiarmc has 8965 (96.0%) missing valuesMissing
bmxwaist has 1134 (12.1%) missing valuesMissing
bmiwaist has 8882 (95.1%) missing valuesMissing
bmxsad1 has 2543 (27.2%) missing valuesMissing
bmxsad2 has 2543 (27.2%) missing valuesMissing
bmxsad3 has 8940 (95.7%) missing valuesMissing
bmxsad4 has 8940 (95.7%) missing valuesMissing
bmdavsad has 2543 (27.2%) missing valuesMissing
bmdsadcm has 8853 (94.8%) missing valuesMissing
Unnamed: 0 is uniformly distributedUniform
seqn is uniformly distributedUniform
Unnamed: 0 has unique valuesUnique
seqn has unique valuesUnique
bmihead is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-08-17 09:37:09.034543
Analysis finished2023-08-17 09:39:01.343340
Duration1 minute and 52.31 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct9338
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4669.5
Minimum1
Maximum9338
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:01.842341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile467.85
Q12335.25
median4669.5
Q37003.75
95-th percentile8871.15
Maximum9338
Range9337
Interquartile range (IQR)4668.5

Descriptive statistics

Standard deviation2695.7927
Coefficient of variation (CV)0.57731936
Kurtosis-1.2
Mean4669.5
Median Absolute Deviation (MAD)2334.5
Skewness0
Sum43603791
Variance7267298.5
MonotonicityStrictly increasing
2023-08-17T16:39:02.341341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
6202 1
 
< 0.1%
6222 1
 
< 0.1%
6223 1
 
< 0.1%
6224 1
 
< 0.1%
6225 1
 
< 0.1%
6226 1
 
< 0.1%
6227 1
 
< 0.1%
6228 1
 
< 0.1%
6229 1
 
< 0.1%
Other values (9328) 9328
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
9338 1
< 0.1%
9337 1
< 0.1%
9336 1
< 0.1%
9335 1
< 0.1%
9334 1
< 0.1%
9333 1
< 0.1%
9332 1
< 0.1%
9331 1
< 0.1%
9330 1
< 0.1%
9329 1
< 0.1%

seqn
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct9338
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67042.749
Minimum62161
Maximum71916
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:02.848338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum62161
5-th percentile62644.85
Q164605.25
median67048.5
Q369478.75
95-th percentile71435.15
Maximum71916
Range9755
Interquartile range (IQR)4873.5

Descriptive statistics

Standard deviation2817.0377
Coefficient of variation (CV)0.042018529
Kurtosis-1.1999382
Mean67042.749
Median Absolute Deviation (MAD)2437
Skewness-0.0031914188
Sum6.2604519 × 108
Variance7935701.5
MonotonicityStrictly increasing
2023-08-17T16:39:03.369340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62161 1
 
< 0.1%
68650 1
 
< 0.1%
68670 1
 
< 0.1%
68671 1
 
< 0.1%
68672 1
 
< 0.1%
68673 1
 
< 0.1%
68674 1
 
< 0.1%
68675 1
 
< 0.1%
68676 1
 
< 0.1%
68677 1
 
< 0.1%
Other values (9328) 9328
99.9%
ValueCountFrequency (%)
62161 1
< 0.1%
62162 1
< 0.1%
62163 1
< 0.1%
62164 1
< 0.1%
62165 1
< 0.1%
62166 1
< 0.1%
62167 1
< 0.1%
62168 1
< 0.1%
62169 1
< 0.1%
62170 1
< 0.1%
ValueCountFrequency (%)
71916 1
< 0.1%
71915 1
< 0.1%
71914 1
< 0.1%
71913 1
< 0.1%
71912 1
< 0.1%
71911 1
< 0.1%
71910 1
< 0.1%
71909 1
< 0.1%
71908 1
< 0.1%
71907 1
< 0.1%

bmdstats
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.1 KiB
1
8520 
3
 
433
2
 
310
4
 
75

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters9338
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8520
91.2%
3 433
 
4.6%
2 310
 
3.3%
4 75
 
0.8%

Length

2023-08-17T16:39:03.831343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T16:39:04.163339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8520
91.2%
3 433
 
4.6%
2 310
 
3.3%
4 75
 
0.8%

Most occurring characters

ValueCountFrequency (%)
1 8520
91.2%
3 433
 
4.6%
2 310
 
3.3%
4 75
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 9338
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8520
91.2%
3 433
 
4.6%
2 310
 
3.3%
4 75
 
0.8%

Most occurring scripts

ValueCountFrequency (%)
Common 9338
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8520
91.2%
3 433
 
4.6%
2 310
 
3.3%
4 75
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9338
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8520
91.2%
3 433
 
4.6%
2 310
 
3.3%
4 75
 
0.8%

bmxwt
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1334
Distinct (%)14.4%
Missing95
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean61.528919
Minimum3.6
Maximum216.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:04.574344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.6
5-th percentile11
Q136
median64.5
Q382.7
95-th percentile110.7
Maximum216.1
Range212.5
Interquartile range (IQR)46.7

Descriptive statistics

Standard deviation31.827296
Coefficient of variation (CV)0.51727378
Kurtosis-0.13248095
Mean61.528919
Median Absolute Deviation (MAD)21.2
Skewness0.13791033
Sum568711.8
Variance1012.9768
MonotonicityNot monotonic
2023-08-17T16:39:05.101339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.9 24
 
0.3%
65.3 24
 
0.3%
65.5 22
 
0.2%
74.5 21
 
0.2%
60.6 21
 
0.2%
56.7 20
 
0.2%
67.6 20
 
0.2%
70.7 20
 
0.2%
83.2 20
 
0.2%
67.3 19
 
0.2%
Other values (1324) 9032
96.7%
(Missing) 95
 
1.0%
ValueCountFrequency (%)
3.6 1
 
< 0.1%
3.8 1
 
< 0.1%
3.9 2
< 0.1%
4 4
< 0.1%
4.2 1
 
< 0.1%
4.3 3
< 0.1%
4.4 4
< 0.1%
4.5 4
< 0.1%
4.6 4
< 0.1%
4.7 1
 
< 0.1%
ValueCountFrequency (%)
216.1 1
< 0.1%
203.5 1
< 0.1%
198.7 1
< 0.1%
195.7 1
< 0.1%
193.7 1
< 0.1%
188.5 1
< 0.1%
187.7 1
< 0.1%
187.5 1
< 0.1%
184.5 1
< 0.1%
181.4 2
< 0.1%

bmiwt
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct3
Distinct (%)0.8%
Missing8959
Missing (%)95.9%
Memory size73.1 KiB
3.0
348 
4.0
 
16
1.0
 
15

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1137
Distinct characters5
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row3.0
3rd row4.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 348
 
3.7%
4.0 16
 
0.2%
1.0 15
 
0.2%
(Missing) 8959
95.9%

Length

2023-08-17T16:39:05.849345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T16:39:06.201340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 348
91.8%
4.0 16
 
4.2%
1.0 15
 
4.0%

Most occurring characters

ValueCountFrequency (%)
. 379
33.3%
0 379
33.3%
3 348
30.6%
4 16
 
1.4%
1 15
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 758
66.7%
Other Punctuation 379
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 379
50.0%
3 348
45.9%
4 16
 
2.1%
1 15
 
2.0%
Other Punctuation
ValueCountFrequency (%)
. 379
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1137
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 379
33.3%
0 379
33.3%
3 348
30.6%
4 16
 
1.4%
1 15
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1137
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 379
33.3%
0 379
33.3%
3 348
30.6%
4 16
 
1.4%
1 15
 
1.3%

bmxrecum
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct478
Distinct (%)44.3%
Missing8259
Missing (%)88.4%
Infinite0
Infinite (%)0.0%
Mean82.634291
Minimum48.3
Maximum115.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:06.595341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48.3
5-th percentile58.39
Q170.6
median85.2
Q394.75
95-th percentile102.91
Maximum115.6
Range67.3
Interquartile range (IQR)24.15

Descriptive statistics

Standard deviation14.393892
Coefficient of variation (CV)0.17418788
Kurtosis-0.98588627
Mean82.634291
Median Absolute Deviation (MAD)11.7
Skewness-0.25362016
Sum89162.4
Variance207.18413
MonotonicityNot monotonic
2023-08-17T16:39:07.087341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
96.8 10
 
0.1%
88.1 9
 
0.1%
87.9 7
 
0.1%
93.6 7
 
0.1%
96 7
 
0.1%
84.8 6
 
0.1%
88 6
 
0.1%
95.4 6
 
0.1%
99.4 6
 
0.1%
89.3 6
 
0.1%
Other values (468) 1009
 
10.8%
(Missing) 8259
88.4%
ValueCountFrequency (%)
48.3 1
< 0.1%
50 1
< 0.1%
50.3 1
< 0.1%
50.6 1
< 0.1%
51 1
< 0.1%
51.6 1
< 0.1%
51.7 1
< 0.1%
51.9 1
< 0.1%
52.9 1
< 0.1%
53 1
< 0.1%
ValueCountFrequency (%)
115.6 1
< 0.1%
112.2 1
< 0.1%
109.7 1
< 0.1%
109.6 1
< 0.1%
109.4 1
< 0.1%
109.1 1
< 0.1%
108.7 2
< 0.1%
108.6 1
< 0.1%
108.5 2
< 0.1%
108.2 1
< 0.1%

bmirecum
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)3.2%
Missing9307
Missing (%)99.7%
Memory size73.1 KiB
1.0
31 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters93
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 31
 
0.3%
(Missing) 9307
99.7%

Length

2023-08-17T16:39:07.544343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T16:39:07.869341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 31
100.0%

Most occurring characters

ValueCountFrequency (%)
1 31
33.3%
. 31
33.3%
0 31
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
66.7%
Other Punctuation 31
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 31
50.0%
0 31
50.0%
Other Punctuation
ValueCountFrequency (%)
. 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 31
33.3%
. 31
33.3%
0 31
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 31
33.3%
. 31
33.3%
0 31
33.3%

bmxhead
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct93
Distinct (%)39.4%
Missing9102
Missing (%)97.5%
Infinite0
Infinite (%)0.0%
Mean41.286017
Minimum34.6
Maximum48.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:08.246340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.6
5-th percentile37.075
Q139.4
median41.45
Q343.025
95-th percentile45
Maximum48.4
Range13.8
Interquartile range (IQR)3.625

Descriptive statistics

Standard deviation2.5813932
Coefficient of variation (CV)0.062524637
Kurtosis-0.3457512
Mean41.286017
Median Absolute Deviation (MAD)1.9
Skewness-0.026397893
Sum9743.5
Variance6.6635909
MonotonicityNot monotonic
2023-08-17T16:39:08.762340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.2 9
 
0.1%
42.3 7
 
0.1%
38.4 6
 
0.1%
42.8 6
 
0.1%
44.6 6
 
0.1%
41.5 6
 
0.1%
42.2 6
 
0.1%
41.4 5
 
0.1%
40.5 5
 
0.1%
40.4 5
 
0.1%
Other values (83) 175
 
1.9%
(Missing) 9102
97.5%
ValueCountFrequency (%)
34.6 1
< 0.1%
35.6 1
< 0.1%
35.7 1
< 0.1%
35.8 1
< 0.1%
36.3 1
< 0.1%
36.4 1
< 0.1%
36.6 1
< 0.1%
36.7 2
< 0.1%
36.8 2
< 0.1%
37 1
< 0.1%
ValueCountFrequency (%)
48.4 1
 
< 0.1%
48 2
< 0.1%
46.7 1
 
< 0.1%
46.5 1
 
< 0.1%
45.9 1
 
< 0.1%
45.8 1
 
< 0.1%
45.4 2
< 0.1%
45.2 1
 
< 0.1%
45.1 1
 
< 0.1%
45 3
< 0.1%

bmihead
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing9338
Missing (%)100.0%
Memory size73.1 KiB

bmxht
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1070
Distinct (%)12.4%
Missing723
Missing (%)7.7%
Infinite0
Infinite (%)0.0%
Mean155.42537
Minimum82
Maximum204.5
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:09.386342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82
5-th percentile100.87
Q1148.9
median162.1
Q3171.3
95-th percentile182.23
Maximum204.5
Range122.5
Interquartile range (IQR)22.4

Descriptive statistics

Standard deviation23.782055
Coefficient of variation (CV)0.15301269
Kurtosis0.77536218
Mean155.42537
Median Absolute Deviation (MAD)10.4
Skewness-1.2169516
Sum1338989.6
Variance565.58613
MonotonicityNot monotonic
2023-08-17T16:39:09.918339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
173.2 35
 
0.4%
166.9 35
 
0.4%
160.8 34
 
0.4%
169.1 34
 
0.4%
164.9 33
 
0.4%
160.3 33
 
0.4%
159.6 33
 
0.4%
167.1 33
 
0.4%
173.6 33
 
0.4%
165.6 31
 
0.3%
Other values (1060) 8281
88.7%
(Missing) 723
 
7.7%
ValueCountFrequency (%)
82 2
< 0.1%
82.4 1
 
< 0.1%
82.8 1
 
< 0.1%
83.5 1
 
< 0.1%
83.7 2
< 0.1%
83.8 4
< 0.1%
84 1
 
< 0.1%
84.1 1
 
< 0.1%
84.2 1
 
< 0.1%
84.3 1
 
< 0.1%
ValueCountFrequency (%)
204.5 1
< 0.1%
202.7 1
< 0.1%
200.4 1
< 0.1%
199.9 1
< 0.1%
199.5 1
< 0.1%
199.4 1
< 0.1%
197.2 1
< 0.1%
196 1
< 0.1%
195.8 1
< 0.1%
195.6 2
< 0.1%

bmiht
Categorical

HIGH CORRELATION  MISSING 

Distinct2
Distinct (%)0.7%
Missing9070
Missing (%)97.1%
Memory size73.1 KiB
3.0
212 
1.0
56 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters804
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3.0
2nd row1.0
3rd row1.0
4th row3.0
5th row3.0

Common Values

ValueCountFrequency (%)
3.0 212
 
2.3%
1.0 56
 
0.6%
(Missing) 9070
97.1%

Length

2023-08-17T16:39:10.535341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T16:39:10.970343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3.0 212
79.1%
1.0 56
 
20.9%

Most occurring characters

ValueCountFrequency (%)
. 268
33.3%
0 268
33.3%
3 212
26.4%
1 56
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 536
66.7%
Other Punctuation 268
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 268
50.0%
3 212
39.6%
1 56
 
10.4%
Other Punctuation
ValueCountFrequency (%)
. 268
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 804
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 268
33.3%
0 268
33.3%
3 212
26.4%
1 56
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 804
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 268
33.3%
0 268
33.3%
3 212
26.4%
1 56
 
7.0%

bmxbmi
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct417
Distinct (%)4.8%
Missing736
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean25.339537
Minimum12.4
Maximum82.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:11.427340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12.4
5-th percentile15.1
Q119.3
median24.5
Q329.8
95-th percentile39.2
Maximum82.1
Range69.7
Interquartile range (IQR)10.5

Descriptive statistics

Standard deviation7.7171028
Coefficient of variation (CV)0.3045479
Kurtosis1.9261668
Mean25.339537
Median Absolute Deviation (MAD)5.2
Skewness0.97579776
Sum217970.7
Variance59.553676
MonotonicityNot monotonic
2023-08-17T16:39:12.057343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16.2 64
 
0.7%
23.2 62
 
0.7%
15.8 60
 
0.6%
22.6 60
 
0.6%
16 58
 
0.6%
23.8 57
 
0.6%
16.1 56
 
0.6%
24.7 55
 
0.6%
22 55
 
0.6%
24.5 55
 
0.6%
Other values (407) 8020
85.9%
(Missing) 736
 
7.9%
ValueCountFrequency (%)
12.4 1
 
< 0.1%
12.5 2
 
< 0.1%
12.6 1
 
< 0.1%
12.7 2
 
< 0.1%
12.8 2
 
< 0.1%
12.9 4
 
< 0.1%
13 3
 
< 0.1%
13.1 2
 
< 0.1%
13.2 5
0.1%
13.3 10
0.1%
ValueCountFrequency (%)
82.1 1
< 0.1%
80.6 1
< 0.1%
69 1
< 0.1%
68.7 1
< 0.1%
67.3 1
< 0.1%
66.2 1
< 0.1%
65.1 1
< 0.1%
63.3 1
< 0.1%
62.8 1
< 0.1%
62 1
< 0.1%

bmdbmic
Categorical

MISSING 

Distinct4
Distinct (%)0.1%
Missing5983
Missing (%)64.1%
Memory size73.1 KiB
2.0
2155 
4.0
593 
3.0
481 
1.0
 
126

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters10065
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2.0
2nd row2.0
3rd row3.0
4th row2.0
5th row2.0

Common Values

ValueCountFrequency (%)
2.0 2155
 
23.1%
4.0 593
 
6.4%
3.0 481
 
5.2%
1.0 126
 
1.3%
(Missing) 5983
64.1%

Length

2023-08-17T16:39:12.618343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T16:39:13.065340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2.0 2155
64.2%
4.0 593
 
17.7%
3.0 481
 
14.3%
1.0 126
 
3.8%

Most occurring characters

ValueCountFrequency (%)
. 3355
33.3%
0 3355
33.3%
2 2155
21.4%
4 593
 
5.9%
3 481
 
4.8%
1 126
 
1.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6710
66.7%
Other Punctuation 3355
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3355
50.0%
2 2155
32.1%
4 593
 
8.8%
3 481
 
7.2%
1 126
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 3355
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10065
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3355
33.3%
0 3355
33.3%
2 2155
21.4%
4 593
 
5.9%
3 481
 
4.8%
1 126
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10065
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3355
33.3%
0 3355
33.3%
2 2155
21.4%
4 593
 
5.9%
3 481
 
4.8%
1 126
 
1.3%

bmxleg
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct235
Distinct (%)3.4%
Missing2383
Missing (%)25.5%
Infinite0
Infinite (%)0.0%
Mean38.185564
Minimum24.8
Maximum52.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:13.547342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24.8
5-th percentile31.1
Q135.5
median38.3
Q341
95-th percentile44.7
Maximum52.8
Range28
Interquartile range (IQR)5.5

Descriptive statistics

Standard deviation4.0649393
Coefficient of variation (CV)0.10645225
Kurtosis-0.14999449
Mean38.185564
Median Absolute Deviation (MAD)2.7
Skewness-0.11565599
Sum265580.6
Variance16.523732
MonotonicityNot monotonic
2023-08-17T16:39:14.279391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 134
 
1.4%
39 130
 
1.4%
38 128
 
1.4%
40 119
 
1.3%
39.5 110
 
1.2%
36 105
 
1.1%
38.2 100
 
1.1%
42 97
 
1.0%
40.2 94
 
1.0%
38.5 92
 
1.0%
Other values (225) 5846
62.6%
(Missing) 2383
25.5%
ValueCountFrequency (%)
24.8 1
 
< 0.1%
25.1 1
 
< 0.1%
25.5 3
< 0.1%
26 1
 
< 0.1%
26.2 2
< 0.1%
26.3 1
 
< 0.1%
26.4 1
 
< 0.1%
26.5 1
 
< 0.1%
26.6 2
< 0.1%
26.8 2
< 0.1%
ValueCountFrequency (%)
52.8 1
 
< 0.1%
52.6 1
 
< 0.1%
51.3 1
 
< 0.1%
51.2 1
 
< 0.1%
51.1 1
 
< 0.1%
50.2 1
 
< 0.1%
50 1
 
< 0.1%
49.3 2
< 0.1%
49.2 1
 
< 0.1%
49 3
< 0.1%

bmileg
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing8984
Missing (%)96.2%
Memory size73.1 KiB
1.0
354 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1062
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 354
 
3.8%
(Missing) 8984
96.2%

Length

2023-08-17T16:39:14.984389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T16:39:15.464391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 354
100.0%

Most occurring characters

ValueCountFrequency (%)
1 354
33.3%
. 354
33.3%
0 354
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 708
66.7%
Other Punctuation 354
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 354
50.0%
0 354
50.0%
Other Punctuation
ValueCountFrequency (%)
. 354
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1062
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 354
33.3%
. 354
33.3%
0 354
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1062
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 354
33.3%
. 354
33.3%
0 354
33.3%

bmxarml
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct352
Distinct (%)4.0%
Missing512
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean32.878881
Minimum10
Maximum48.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:15.893392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile17
Q129.9
median35.3
Q338
95-th percentile41.2
Maximum48.1
Range38.1
Interquartile range (IQR)8.1

Descriptive statistics

Standard deviation7.4523161
Coefficient of variation (CV)0.22665967
Kurtosis0.36866563
Mean32.878881
Median Absolute Deviation (MAD)3.3
Skewness-1.1116883
Sum290189
Variance55.537016
MonotonicityNot monotonic
2023-08-17T16:39:16.431387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37 215
 
2.3%
36 188
 
2.0%
38 180
 
1.9%
35 171
 
1.8%
39 140
 
1.5%
36.5 128
 
1.4%
37.5 126
 
1.3%
34 124
 
1.3%
40 121
 
1.3%
35.5 110
 
1.2%
Other values (342) 7323
78.4%
(Missing) 512
 
5.5%
ValueCountFrequency (%)
10 1
 
< 0.1%
10.2 1
 
< 0.1%
10.4 1
 
< 0.1%
10.5 1
 
< 0.1%
10.8 4
< 0.1%
11 6
0.1%
11.1 1
 
< 0.1%
11.2 1
 
< 0.1%
11.4 2
 
< 0.1%
11.5 3
< 0.1%
ValueCountFrequency (%)
48.1 1
< 0.1%
47.7 1
< 0.1%
47.5 1
< 0.1%
47.2 1
< 0.1%
46.7 1
< 0.1%
46.5 1
< 0.1%
45.7 1
< 0.1%
45.6 1
< 0.1%
45.5 2
< 0.1%
45.3 1
< 0.1%

bmiarml
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing8969
Missing (%)96.0%
Memory size73.1 KiB
1.0
369 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1107
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 369
 
4.0%
(Missing) 8969
96.0%

Length

2023-08-17T16:39:16.964390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T16:39:17.333391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 369
100.0%

Most occurring characters

ValueCountFrequency (%)
1 369
33.3%
. 369
33.3%
0 369
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 738
66.7%
Other Punctuation 369
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 369
50.0%
0 369
50.0%
Other Punctuation
ValueCountFrequency (%)
. 369
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1107
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 369
33.3%
. 369
33.3%
0 369
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1107
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 369
33.3%
. 369
33.3%
0 369
33.3%

bmxarmc
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct394
Distinct (%)4.5%
Missing512
Missing (%)5.5%
Infinite0
Infinite (%)0.0%
Mean28.326615
Minimum10.5
Maximum58.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:17.868394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10.5
5-th percentile15.4
Q122.2
median29.25
Q334
95-th percentile40.3
Maximum58.1
Range47.6
Interquartile range (IQR)11.8

Descriptive statistics

Standard deviation7.8962733
Coefficient of variation (CV)0.2787581
Kurtosis-0.57312066
Mean28.326615
Median Absolute Deviation (MAD)5.45
Skewness-0.056638297
Sum250010.7
Variance62.351132
MonotonicityNot monotonic
2023-08-17T16:39:18.429918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.5 73
 
0.8%
30 73
 
0.8%
29 72
 
0.8%
32 72
 
0.8%
32.5 70
 
0.7%
31 70
 
0.7%
31.5 68
 
0.7%
36 67
 
0.7%
27 67
 
0.7%
33 67
 
0.7%
Other values (384) 8127
87.0%
(Missing) 512
 
5.5%
ValueCountFrequency (%)
10.5 1
 
< 0.1%
11.4 1
 
< 0.1%
11.5 2
< 0.1%
11.6 1
 
< 0.1%
11.9 1
 
< 0.1%
12 1
 
< 0.1%
12.1 4
< 0.1%
12.2 2
< 0.1%
12.3 1
 
< 0.1%
12.4 1
 
< 0.1%
ValueCountFrequency (%)
58.1 1
< 0.1%
57.3 1
< 0.1%
56.7 1
< 0.1%
54.9 1
< 0.1%
54.6 1
< 0.1%
53.8 1
< 0.1%
52.9 1
< 0.1%
52.1 1
< 0.1%
51.6 1
< 0.1%
51.5 2
< 0.1%

bmiarmc
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.3%
Missing8965
Missing (%)96.0%
Memory size73.1 KiB
1.0
373 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1119
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 373
 
4.0%
(Missing) 8965
96.0%

Length

2023-08-17T16:39:18.858915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T16:39:19.177916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 373
100.0%

Most occurring characters

ValueCountFrequency (%)
1 373
33.3%
. 373
33.3%
0 373
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 746
66.7%
Other Punctuation 373
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 373
50.0%
0 373
50.0%
Other Punctuation
ValueCountFrequency (%)
. 373
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1119
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 373
33.3%
. 373
33.3%
0 373
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1119
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 373
33.3%
. 373
33.3%
0 373
33.3%

bmxwaist
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1008
Distinct (%)12.3%
Missing1134
Missing (%)12.1%
Infinite0
Infinite (%)0.0%
Mean86.223976
Minimum38.7
Maximum176
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:19.821914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38.7
5-th percentile50
Q170.275
median86.9
Q3101.5
95-th percentile122.785
Maximum176
Range137.3
Interquartile range (IQR)31.225

Descriptive statistics

Standard deviation22.365236
Coefficient of variation (CV)0.25938534
Kurtosis-0.33483133
Mean86.223976
Median Absolute Deviation (MAD)15.4
Skewness0.14795647
Sum707381.5
Variance500.20377
MonotonicityNot monotonic
2023-08-17T16:39:20.290915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
97 40
 
0.4%
74 34
 
0.4%
89 31
 
0.3%
80 31
 
0.3%
83 31
 
0.3%
52 29
 
0.3%
78 27
 
0.3%
99 27
 
0.3%
77 26
 
0.3%
90 26
 
0.3%
Other values (998) 7902
84.6%
(Missing) 1134
 
12.1%
ValueCountFrequency (%)
38.7 1
< 0.1%
39.7 1
< 0.1%
39.8 1
< 0.1%
40.2 1
< 0.1%
40.5 1
< 0.1%
40.6 1
< 0.1%
41 1
< 0.1%
41.1 1
< 0.1%
42 1
< 0.1%
42.5 1
< 0.1%
ValueCountFrequency (%)
176 1
< 0.1%
172.2 1
< 0.1%
170.5 1
< 0.1%
170.3 1
< 0.1%
163.9 1
< 0.1%
163.7 1
< 0.1%
163.5 1
< 0.1%
161.6 1
< 0.1%
160.5 1
< 0.1%
158.8 1
< 0.1%

bmiwaist
Categorical

CONSTANT  MISSING 

Distinct1
Distinct (%)0.2%
Missing8882
Missing (%)95.1%
Memory size73.1 KiB
1.0
456 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1368
Distinct characters3
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 456
 
4.9%
(Missing) 8882
95.1%

Length

2023-08-17T16:39:20.733912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T16:39:21.033916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 456
100.0%

Most occurring characters

ValueCountFrequency (%)
1 456
33.3%
. 456
33.3%
0 456
33.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 912
66.7%
Other Punctuation 456
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 456
50.0%
0 456
50.0%
Other Punctuation
ValueCountFrequency (%)
. 456
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1368
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 456
33.3%
. 456
33.3%
0 456
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 456
33.3%
. 456
33.3%
0 456
33.3%

bmxsad1
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct267
Distinct (%)3.9%
Missing2543
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean21.063532
Minimum10
Maximum40.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:21.394918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14
Q117.4
median20.6
Q324.3
95-th percentile29.8
Maximum40.4
Range30.4
Interquartile range (IQR)6.9

Descriptive statistics

Standard deviation4.8570347
Coefficient of variation (CV)0.23058976
Kurtosis0.019307145
Mean21.063532
Median Absolute Deviation (MAD)3.4
Skewness0.51679394
Sum143126.7
Variance23.590786
MonotonicityNot monotonic
2023-08-17T16:39:21.894915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 94
 
1.0%
16.8 87
 
0.9%
17 86
 
0.9%
21.8 80
 
0.9%
18 78
 
0.8%
19 77
 
0.8%
20 76
 
0.8%
22 76
 
0.8%
20.8 73
 
0.8%
19.8 72
 
0.8%
Other values (257) 5996
64.2%
(Missing) 2543
27.2%
ValueCountFrequency (%)
10 1
 
< 0.1%
10.1 1
 
< 0.1%
10.2 1
 
< 0.1%
10.5 3
< 0.1%
10.9 2
 
< 0.1%
11.2 2
 
< 0.1%
11.3 1
 
< 0.1%
11.4 5
0.1%
11.5 4
< 0.1%
11.6 2
 
< 0.1%
ValueCountFrequency (%)
40.4 1
 
< 0.1%
39.5 1
 
< 0.1%
39 1
 
< 0.1%
38.8 1
 
< 0.1%
38.3 1
 
< 0.1%
37.9 1
 
< 0.1%
37.8 3
< 0.1%
37.7 1
 
< 0.1%
37.6 2
< 0.1%
37.5 1
 
< 0.1%

bmxsad2
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct273
Distinct (%)4.0%
Missing2543
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean21.048094
Minimum9.9
Maximum40.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:22.348914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9.9
5-th percentile13.9
Q117.4
median20.6
Q324.3
95-th percentile29.73
Maximum40.8
Range30.9
Interquartile range (IQR)6.9

Descriptive statistics

Standard deviation4.8730574
Coefficient of variation (CV)0.23152013
Kurtosis0.0089049299
Mean21.048094
Median Absolute Deviation (MAD)3.4
Skewness0.51938351
Sum143021.8
Variance23.746689
MonotonicityNot monotonic
2023-08-17T16:39:22.839920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19 91
 
1.0%
21 87
 
0.9%
22 81
 
0.9%
20.8 73
 
0.8%
16.8 73
 
0.8%
18.8 72
 
0.8%
20 72
 
0.8%
17 68
 
0.7%
19.8 67
 
0.7%
17.8 67
 
0.7%
Other values (263) 6044
64.7%
(Missing) 2543
27.2%
ValueCountFrequency (%)
9.9 1
 
< 0.1%
10.4 2
< 0.1%
10.7 2
< 0.1%
10.9 1
 
< 0.1%
11 2
< 0.1%
11.1 2
< 0.1%
11.2 2
< 0.1%
11.3 1
 
< 0.1%
11.4 1
 
< 0.1%
11.5 3
< 0.1%
ValueCountFrequency (%)
40.8 1
< 0.1%
39.9 1
< 0.1%
39.1 1
< 0.1%
38.6 1
< 0.1%
38 1
< 0.1%
37.9 1
< 0.1%
37.8 2
< 0.1%
37.7 1
< 0.1%
37.6 1
< 0.1%
37.5 1
< 0.1%

bmxsad3
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct161
Distinct (%)40.5%
Missing8940
Missing (%)95.7%
Infinite0
Infinite (%)0.0%
Mean22.355276
Minimum11
Maximum36.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:23.327917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile14.4
Q118.825
median22.45
Q325.5
95-th percentile31.075
Maximum36.4
Range25.4
Interquartile range (IQR)6.675

Descriptive statistics

Standard deviation4.9730822
Coefficient of variation (CV)0.22245675
Kurtosis-0.31660413
Mean22.355276
Median Absolute Deviation (MAD)3.35
Skewness0.19751016
Sum8897.4
Variance24.731546
MonotonicityNot monotonic
2023-08-17T16:39:23.973915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23 10
 
0.1%
20.3 7
 
0.1%
22.5 7
 
0.1%
24 6
 
0.1%
22.7 6
 
0.1%
19.8 6
 
0.1%
22.4 6
 
0.1%
19.6 6
 
0.1%
28.7 6
 
0.1%
19.3 6
 
0.1%
Other values (151) 332
 
3.6%
(Missing) 8940
95.7%
ValueCountFrequency (%)
11 1
 
< 0.1%
12 1
 
< 0.1%
12.5 2
< 0.1%
12.7 1
 
< 0.1%
12.8 1
 
< 0.1%
13 1
 
< 0.1%
13.2 2
< 0.1%
13.3 3
< 0.1%
13.5 1
 
< 0.1%
13.7 1
 
< 0.1%
ValueCountFrequency (%)
36.4 1
< 0.1%
36 1
< 0.1%
35 2
< 0.1%
33.9 1
< 0.1%
33.8 1
< 0.1%
33 1
< 0.1%
32.9 1
< 0.1%
32.7 1
< 0.1%
32.6 1
< 0.1%
32.5 1
< 0.1%

bmxsad4
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct171
Distinct (%)43.0%
Missing8940
Missing (%)95.7%
Infinite0
Infinite (%)0.0%
Mean22.371106
Minimum11.1
Maximum36.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:24.449916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11.1
5-th percentile14.47
Q118.8
median22.3
Q325.5
95-th percentile31.415
Maximum36.4
Range25.3
Interquartile range (IQR)6.7

Descriptive statistics

Standard deviation4.9791499
Coefficient of variation (CV)0.22257058
Kurtosis-0.3147557
Mean22.371106
Median Absolute Deviation (MAD)3.4
Skewness0.22066048
Sum8903.7
Variance24.791934
MonotonicityNot monotonic
2023-08-17T16:39:24.944916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.5 8
 
0.1%
23.8 7
 
0.1%
22.9 6
 
0.1%
22.7 6
 
0.1%
23 6
 
0.1%
25.8 6
 
0.1%
17.5 5
 
0.1%
20.6 5
 
0.1%
24.2 5
 
0.1%
22.2 5
 
0.1%
Other values (161) 339
 
3.6%
(Missing) 8940
95.7%
ValueCountFrequency (%)
11.1 1
 
< 0.1%
11.8 1
 
< 0.1%
12.6 1
 
< 0.1%
12.7 1
 
< 0.1%
12.9 1
 
< 0.1%
13.2 3
< 0.1%
13.3 3
< 0.1%
13.5 1
 
< 0.1%
13.6 1
 
< 0.1%
13.7 1
 
< 0.1%
ValueCountFrequency (%)
36.4 1
< 0.1%
36.2 1
< 0.1%
35.4 1
< 0.1%
34.7 1
< 0.1%
34 1
< 0.1%
33.7 1
< 0.1%
33.2 1
< 0.1%
32.8 1
< 0.1%
32.7 1
< 0.1%
32.5 2
< 0.1%

bmdavsad
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct271
Distinct (%)4.0%
Missing2543
Missing (%)27.2%
Infinite0
Infinite (%)0.0%
Mean21.070052
Minimum10
Maximum40.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size73.1 KiB
2023-08-17T16:39:25.420913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile13.9
Q117.4
median20.6
Q324.3
95-th percentile29.8
Maximum40.6
Range30.6
Interquartile range (IQR)6.9

Descriptive statistics

Standard deviation4.8635051
Coefficient of variation (CV)0.2308255
Kurtosis0.0049457381
Mean21.070052
Median Absolute Deviation (MAD)3.4
Skewness0.51611955
Sum143171
Variance23.653682
MonotonicityNot monotonic
2023-08-17T16:39:25.951914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21.9 71
 
0.8%
18.9 70
 
0.7%
17.9 69
 
0.7%
16.5 68
 
0.7%
20.9 66
 
0.7%
16.9 61
 
0.7%
17 61
 
0.7%
19.9 61
 
0.7%
21.2 60
 
0.6%
18 60
 
0.6%
Other values (261) 6148
65.8%
(Missing) 2543
27.2%
ValueCountFrequency (%)
10 1
 
< 0.1%
10.3 1
 
< 0.1%
10.5 1
 
< 0.1%
10.6 1
 
< 0.1%
10.7 1
 
< 0.1%
10.8 1
 
< 0.1%
11 1
 
< 0.1%
11.1 2
< 0.1%
11.3 3
< 0.1%
11.4 3
< 0.1%
ValueCountFrequency (%)
40.6 1
< 0.1%
39.7 1
< 0.1%
39.1 1
< 0.1%
38.7 1
< 0.1%
37.9 2
< 0.1%
37.8 2
< 0.1%
37.7 2
< 0.1%
37.5 2
< 0.1%
37.4 1
< 0.1%
37.2 1
< 0.1%

bmdsadcm
Categorical

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct5
Distinct (%)1.0%
Missing8853
Missing (%)94.8%
Memory size73.1 KiB
1.0
427 
4.0
 
22
2.0
 
19
5.0
 
10
3.0
 
7

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters1455
Distinct characters7
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4.0
2nd row1.0
3rd row1.0
4th row1.0
5th row1.0

Common Values

ValueCountFrequency (%)
1.0 427
 
4.6%
4.0 22
 
0.2%
2.0 19
 
0.2%
5.0 10
 
0.1%
3.0 7
 
0.1%
(Missing) 8853
94.8%

Length

2023-08-17T16:39:26.415914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-08-17T16:39:26.769917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1.0 427
88.0%
4.0 22
 
4.5%
2.0 19
 
3.9%
5.0 10
 
2.1%
3.0 7
 
1.4%

Most occurring characters

ValueCountFrequency (%)
. 485
33.3%
0 485
33.3%
1 427
29.3%
4 22
 
1.5%
2 19
 
1.3%
5 10
 
0.7%
3 7
 
0.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 970
66.7%
Other Punctuation 485
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 485
50.0%
1 427
44.0%
4 22
 
2.3%
2 19
 
2.0%
5 10
 
1.0%
3 7
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 485
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1455
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 485
33.3%
0 485
33.3%
1 427
29.3%
4 22
 
1.5%
2 19
 
1.3%
5 10
 
0.7%
3 7
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 485
33.3%
0 485
33.3%
1 427
29.3%
4 22
 
1.5%
2 19
 
1.3%
5 10
 
0.7%
3 7
 
0.5%

Interactions

2023-08-17T16:38:49.461341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:14.567553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:24.100638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:31.586162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:37.464682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:42.636678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:47.893204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:53.761204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:59.028729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:06.400732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:15.483727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:23.578729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:28.865772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:33.974294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:39.470292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:44.242295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:50.117341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:15.033543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:24.594637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:32.172163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:37.783682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:42.946677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:48.213206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:54.070206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:59.332731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:06.933728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:16.094730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:23.882730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:29.185771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:34.277293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:39.776293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:44.527291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:50.529342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:15.552545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:25.092635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:32.488160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:38.118682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:43.243687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:48.632204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:54.381205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:59.818729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:07.481732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:16.614730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:24.211729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:29.500773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:34.590292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:40.092290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:44.833295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:50.885343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:16.378543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:25.598635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:32.769162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:38.445678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:43.563680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:48.969203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:54.691205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:00.225729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:08.090732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:17.079729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:24.531729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:29.850770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:35.245292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:40.412295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:45.163295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:51.267344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:16.961545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:25.926635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:33.117160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:38.758682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:44.077681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:49.416206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:55.042209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:00.506734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:08.666727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:17.634728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:24.865731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:30.117776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:35.489296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:40.680294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:45.403295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:51.603341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:17.563547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:26.268635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:33.463163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:39.126680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:44.428682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:49.762207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:55.330205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:00.924730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:09.134728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:18.101729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:25.133729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:30.377771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:35.752293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:40.936294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:45.693294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:52.019341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:18.099543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:27.042163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:33.799163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:39.443680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:44.685677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:50.157204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:55.678206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:01.399729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:09.673729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:18.617729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:25.501251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:30.697774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:36.053292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:41.252294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:46.008293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:52.958458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:18.636542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:27.442163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:34.150678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:39.798679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:44.958681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:50.545207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:56.028207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:01.927730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:10.203728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:19.143729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:25.878271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:31.047771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:36.380294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:41.580295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:46.318292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:53.375341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:19.133543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:27.940164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:34.483679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:40.088680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:45.226204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:50.856205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:56.345732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:02.416730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:10.685728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:19.664729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:26.227771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:31.360770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:36.707303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:41.862296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:46.630294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:53.732338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:19.715543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:28.447162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:34.829681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:40.523687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:45.614207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:51.211202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:56.685727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:02.895731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:11.258730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:20.178731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:26.578770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:31.686769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:37.035292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:42.161292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:46.916292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:54.215342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:20.286543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:28.793162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:35.444677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:40.925679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:46.016203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:51.532207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:56.996726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:03.357730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:11.764728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:20.694731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:26.891774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:31.983775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:37.336290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:42.431292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:47.328291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:54.761343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:20.965072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:29.396161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:35.762677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:41.277680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:46.336206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:51.900203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:57.334729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:03.851729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:12.292730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:21.202726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:27.244772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:32.319771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:37.843294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:42.748294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:47.777294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:55.285341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:21.676637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:30.017159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:36.118680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:41.565682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:46.620203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:52.230205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:57.676737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:04.359728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:12.896730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:21.758731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:27.567771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:32.650774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:38.180295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:43.043295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:48.226293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:55.693344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:22.353636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:30.330162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:36.460682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:41.849681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:46.962204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:52.846206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:58.015728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:04.883730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:13.512730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:22.381732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:27.898773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:33.014290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:38.518291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:43.326292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:48.541292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:56.144344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:22.953636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:30.816161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:36.769677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:42.116677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:47.265204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:53.149204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:58.330730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:05.391728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:13.984731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:22.840734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:28.208773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:33.319292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:38.824299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:43.629295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:48.832817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:56.595346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:23.507641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:31.248162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:37.106691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:42.369678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:47.553205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:53.450206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:37:58.659729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:05.865729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:14.950730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:23.234730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:28.524773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:33.633295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:39.137292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:43.932292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-08-17T16:38:49.152340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-08-17T16:39:27.112917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 0seqnbmxwtbmxrecumbmxheadbmxhtbmxbmibmxlegbmxarmlbmxarmcbmxwaistbmxsad1bmxsad2bmxsad3bmxsad4bmdavsadbmdstatsbmiwtbmihtbmdbmicbmdsadcm
Unnamed: 01.0001.000-0.0030.001-0.0300.007-0.003-0.0010.0000.0010.001-0.001-0.0020.0530.050-0.0020.0000.0000.0000.0000.000
seqn1.0001.000-0.0030.001-0.0300.007-0.003-0.0010.0000.0010.001-0.001-0.0020.0530.050-0.0020.0050.0000.0000.0000.000
bmxwt-0.003-0.0031.0000.9560.8690.7620.9240.4520.8630.9650.9420.8860.8870.8880.8870.8870.0720.2160.8120.2940.156
bmxrecum0.0010.0010.9561.0000.8550.987-0.028NaN0.9480.6090.470NaNNaNNaNNaNNaN0.1530.0001.0000.1040.000
bmxhead-0.030-0.0300.8690.8551.000NaNNaNNaN0.5100.489NaNNaNNaNNaNNaNNaN0.0001.0000.0000.0000.000
bmxht0.0070.0070.7620.987NaN1.0000.5100.7970.9080.6600.6140.3620.3630.4170.4130.3630.0710.0001.0000.0960.092
bmxbmi-0.003-0.0030.924-0.028NaN0.5101.0000.1550.6440.9420.9510.9220.9220.8910.8910.9230.1170.1721.0000.4230.179
bmxleg-0.001-0.0010.452NaNNaN0.7970.1551.0000.7030.3310.2020.1960.1970.1860.1810.1970.0710.0000.0890.0840.106
bmxarml0.0000.0000.8630.9480.5100.9080.6440.7031.0000.8010.7250.5390.5390.5580.5550.5400.0640.3010.7000.1370.144
bmxarmc0.0010.0010.9650.6090.4890.6600.9420.3310.8011.0000.9220.8650.8650.8500.8500.8650.1260.2340.6660.3760.234
bmxwaist0.0010.0010.9420.470NaN0.6140.9510.2020.7250.9221.0000.9610.9600.9500.9490.9610.1490.0000.6190.3860.358
bmxsad1-0.001-0.0010.886NaNNaN0.3620.9220.1960.5390.8650.9611.0000.9980.9850.9840.9990.1300.0000.0000.4700.414
bmxsad2-0.002-0.0020.887NaNNaN0.3630.9220.1970.5390.8650.9600.9981.0000.9930.9920.9990.1270.0000.0000.4680.419
bmxsad30.0530.0530.888NaNNaN0.4170.8910.1860.5580.8500.9500.9850.9931.0000.9970.9990.3060.0001.0000.4180.000
bmxsad40.0500.0500.887NaNNaN0.4130.8910.1810.5550.8500.9490.9840.9920.9971.0000.9990.1250.0001.0000.4560.519
bmdavsad-0.002-0.0020.887NaNNaN0.3630.9230.1970.5400.8650.9610.9990.9990.9990.9991.0000.1260.0000.0000.4680.408
bmdstats0.0000.0050.0720.1530.0000.0710.1170.0710.0640.1260.1490.1300.1270.3060.1250.1261.0000.5700.6290.0280.558
bmiwt0.0000.0000.2160.0001.0000.0000.1720.0000.3010.2340.0000.0000.0000.0000.0000.0000.5701.0000.6480.2561.000
bmiht0.0000.0000.8121.0000.0001.0001.0000.0890.7000.6660.6190.0000.0001.0001.0000.0000.6290.6481.000NaN0.000
bmdbmic0.0000.0000.2940.1040.0000.0960.4230.0840.1370.3760.3860.4700.4680.4180.4560.4680.0280.256NaN1.0000.265
bmdsadcm0.0000.0000.1560.0000.0000.0920.1790.1060.1440.2340.3580.4140.4190.0000.5190.4080.5581.0000.0000.2651.000

Missing values

2023-08-17T16:38:57.368341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-08-17T16:38:58.919343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-08-17T16:39:00.075341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0seqnbmdstatsbmxwtbmiwtbmxrecumbmirecumbmxheadbmiheadbmxhtbmihtbmxbmibmdbmicbmxlegbmilegbmxarmlbmiarmlbmxarmcbmiarmcbmxwaistbmiwaistbmxsad1bmxsad2bmxsad3bmxsad4bmdavsadbmdsadcm
0162161169.2NaNNaNNaNNaNNaN172.3NaN23.3NaN40.2NaN35.0NaN32.5NaN81.0NaN17.717.9NaNNaN17.8NaN
1262162112.7NaN95.7NaNNaNNaN94.7NaN14.22.0NaNNaN18.5NaN16.6NaN45.4NaNNaNNaNNaNNaNNaNNaN
2362163149.4NaNNaNNaNNaNNaN168.9NaN17.32.040.3NaN36.3NaN22.0NaN64.6NaN15.615.5NaNNaN15.6NaN
3462164167.2NaNNaNNaNNaNNaN170.1NaN23.2NaN40.5NaN37.2NaN29.3NaN80.1NaN18.318.5NaNNaN18.4NaN
4562165169.1NaNNaNNaNNaNNaN159.4NaN27.23.042.1NaN35.2NaN29.7NaN86.7NaN21.020.8NaNNaN20.9NaN
5662166128.8NaNNaNNaNNaNNaN133.4NaN16.22.031.0NaN28.0NaN19.1NaN59.8NaN13.513.5NaNNaN13.5NaN
6762167110.8NaN79.5NaNNaNNaNNaNNaNNaNNaNNaNNaN16.2NaN15.5NaNNaNNaNNaNNaNNaNNaNNaNNaN
7862168123.6NaNNaNNaNNaNNaN123.6NaN15.42.0NaNNaN24.8NaN17.1NaN54.4NaNNaNNaNNaNNaNNaNNaN
8962169154.6NaNNaNNaNNaNNaN164.8NaN20.1NaN38.7NaN33.4NaN28.5NaN69.6NaN16.416.3NaNNaN16.4NaN
91062170163.5NaNNaNNaNNaNNaN187.0NaN18.22.043.3NaN37.5NaN25.8NaN69.4NaN14.814.7NaNNaN14.8NaN
Unnamed: 0seqnbmdstatsbmxwtbmiwtbmxrecumbmirecumbmxheadbmiheadbmxhtbmihtbmxbmibmdbmicbmxlegbmilegbmxarmlbmiarmlbmxarmcbmiarmcbmxwaistbmiwaistbmxsad1bmxsad2bmxsad3bmxsad4bmdavsadbmdsadcm
9328932971907271.53.0NaNNaNNaNNaN175.4NaN23.2NaNNaN1.0NaN1.0NaN1.0NaN1.0NaNNaNNaNNaNNaN1.0
9329933071908188.7NaNNaNNaNNaNNaN159.0NaN35.1NaN31.3NaN33.6NaN35.8NaN114.6NaN23.323.4NaNNaN23.4NaN
9330933171909192.3NaNNaNNaNNaNNaN177.3NaN29.4NaN40.8NaN38.4NaN35.4NaN100.8NaN23.723.8NaNNaN23.8NaN
933193327191016.7NaN67.6NaN42.2NaNNaNNaNNaNNaNNaNNaN13.0NaN12.6NaNNaNNaNNaNNaNNaNNaNNaNNaN
9332933371911196.7NaNNaNNaNNaNNaN175.8NaN31.3NaN40.1NaN39.1NaN37.8NaN106.6NaN25.425.3NaNNaN25.4NaN
9333933471912187.8NaNNaNNaNNaNNaN177.3NaN27.9NaN39.0NaN38.7NaN33.1NaN104.4NaN24.424.5NaNNaN24.5NaN
9334933571913156.5NaNNaNNaNNaNNaN161.6NaN21.62.039.4NaN35.0NaN27.5NaN79.0NaN17.517.7NaNNaN17.6NaN
9335933671914132.3NaNNaNNaNNaNNaN148.1NaN14.72.035.6NaN32.7NaN20.1NaN59.6NaN12.512.9NaNNaN12.74.0
9336933771915178.4NaNNaNNaNNaNNaN168.8NaN27.5NaN32.9NaN40.1NaN33.4NaN106.6NaN23.122.9NaNNaN23.0NaN
9337933871916152.6NaNNaNNaNNaNNaN164.4NaN19.52.040.1NaN34.8NaN25.9NaN69.2NaN14.414.3NaNNaN14.4NaN